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Applications of Multi-Robotic Arms to Assist Agricultural Production: A Review
Published 2025-06-01Get full text
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A novel method based on improved SFLA for IP information extraction from TEM signals
Published 2025-07-01Get full text
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Finding high posterior density phylogenies by systematically extending a directed acyclic graph
Published 2025-02-01Get full text
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Generation of a Social Network Graph by Using Apache Spark
Published 2016-12-01Get full text
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Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO
Published 2025-02-01Get full text
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Novel neoadjuvant therapies for muscle‐invasive bladder cancer: Systematic review and meta‐analysis
Published 2025-05-01Get full text
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16-channel photonic solver for optimization problems on a silicon chip
Published 2025-03-01Get full text
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Energy-Efficient Trajectory Planning With Joint Device Selection and Power Splitting for mmWaves-Enabled UAV-NOMA Networks
Published 2024-01-01“…In addition, exhaustive and random search benchmarks are provided as baselines for the achievable upper and lower sum-rate levels, respectively. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data
Published 2025-06-01“…Despite advancements in satellite-based radiometry such as NASA’s Soil Moisture Active Passive (SMAP), significant challenges persist in coastal SSS retrieval due to radio frequency interference (RFI), land-sea contamination, and complex interactions of nearshore dynamic processes.MethodThis study proposes a deep neural network (DNN) framework that integrates SMAP L-band brightness temperature data with ancillary oceanographic and geographic parameters such as sea surface temperature, the shortest distance to the coastline (dis) to enhance SSS estimation accuracy in the Yellow and East China Seas. The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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